Transient Stability Preventive Control Based on Graph Convolution Neural Network and Transfer Deep Reinforcement Learning

Tianjing Wang; Yong Tang

doi:10.17775/CSEEJPES.2022.05030

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Transient Stability Preventive Control Based on Graph Convolution Neural Network and Transfer Deep Reinforcement Learning

Regular Papers | 更新时间：2026-02-06

- Transient Stability Preventive Control Based on Graph Convolution Neural Network and Transfer Deep Reinforcement Learning
- Transient Stability Preventive Control Based on Graph Convolution Neural Network and Transfer Deep Reinforcement Learning
- 中国电机工程学会电力与能源系统学报(英文) 2025年11卷第1期页码：136-149
- 作者机构：
  
  1. School of Electrical and Electronic Engineering, Nanyang Technological University,Singapore
  2. Laboratory of Power Grid Safety and Energy Conservation, China Electric Power Research Institute,Beijing,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2022.05030
  中图分类号：
- 纸质出版：2025
- 稿件说明：
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Tianjing Wang, Yong Tang. Transient Stability Preventive Control Based on Graph Convolution Neural Network and Transfer Deep Reinforcement Learning[J]. 中国电机工程学会电力与能源系统学报(英文), 2025,11(1):136-149.

Tianjing Wang, Yong Tang. Transient Stability Preventive Control Based on Graph Convolution Neural Network and Transfer Deep Reinforcement Learning[J]. CSEE Journal of Power and Energy Systems, 2025, 11(1): 136-149.
Tianjing Wang, Yong Tang. Transient Stability Preventive Control Based on Graph Convolution Neural Network and Transfer Deep Reinforcement Learning[J]. 中国电机工程学会电力与能源系统学报(英文), 2025,11(1):136-149. DOI： 10.17775/CSEEJPES.2022.05030.

Tianjing Wang, Yong Tang. Transient Stability Preventive Control Based on Graph Convolution Neural Network and Transfer Deep Reinforcement Learning[J]. CSEE Journal of Power and Energy Systems, 2025, 11(1): 136-149. DOI： 10.17775/CSEEJPES.2022.05030.

摘要

Abstract

This study proposes a new transient-stability preventive control (TSPC) method based on graph convolutional neural networks (GCNN) and transfer deep reinforcement learning (DRL) to address non-convergence problems of traditional optimization algorithms and slow training speed of artificial intelligence algorithms for TSPC. First

a transient stability assessor (TSA) with GCNN is developed to assess current-power-flow state. Sensitivities of the transient-stability index relative to the generators are approximately calculated using TSA; generators with significant influence able to narrow action space are identified. Subsequently

the Markov decision-making process of TSPC is derived by introducing the process of TSPC. A DRL for TSPC is constructed by adding entropy to twin delayed deep deterministic policy gradient (TD3). Knowledge learned by TSA is transferred to DRL based on transfer learning

which improves learning efficiency. Finally

case studies based on the IEEE 39-bus system and an actual power grid prove the effectiveness of the proposed method. Comparisons performed with reference algorithms in literature demonstrate the proposed method has better performance in both control effect and speed.

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